1 Data preparation

1.1 Outline

  • Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded

  • Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.

1.2 Load packages


library(reportfactory)
library(here)
library(rio) 
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)

1.3 Load scripts

These scripts will load:

  • all scripts stored as .R files inside /scripts/
  • all scripts stored as .R files inside /src/

These scripts also contain routines to access the latest clean encrypted data (see next section).


reportfactory::rfh_load_scripts()

1.4 Load clean data

We import the latest NHS pathways data:


x <- import_pathways() %>%
  as_tibble()
x
## # A tibble: 128,816 x 9
##    site_type date       sex    age   ccg_code ccg_name count postcode nhs_region
##    <chr>     <date>     <chr>  <chr> <chr>    <chr>    <int> <chr>    <chr>     
##  1 111       2020-03-18 female 0-18  e380000… nhs_bar…    35 rm13ae   london    
##  2 111       2020-03-18 female 0-18  e380000… nhs_bed…    27 mk454hr  east_of_e…
##  3 111       2020-03-18 female 0-18  e380000… nhs_bla…     9 bb12fd   north_west
##  4 111       2020-03-18 female 0-18  e380000… nhs_bro…    11 br33ql   london    
##  5 111       2020-03-18 female 0-18  e380000… nhs_can…     9 ws111jp  midlands  
##  6 111       2020-03-18 female 0-18  e380000… nhs_cit…    12 n15lz    london    
##  7 111       2020-03-18 female 0-18  e380000… nhs_enf…     7 en40dy   london    
##  8 111       2020-03-18 female 0-18  e380000… nhs_ham…     6 dl62uu   north_eas…
##  9 111       2020-03-18 female 0-18  e380000… nhs_har…    24 ts232la  north_eas…
## 10 111       2020-03-18 female 0-18  e380000… nhs_kin…     6 kt11eu   london    
## # … with 128,806 more rows

We also import demographics data for NHS regions in England, used later in our analysis:


path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
##                  nhs_region variable      value
## 1                North West     0-18 0.22538599
## 2  North East and Yorkshire     0-18 0.21876449
## 3                  Midlands     0-18 0.22564656
## 4           East of England     0-18 0.22810783
## 5                    London     0-18 0.23764782
## 6                South East     0-18 0.22458811
## 7                South West     0-18 0.20799797
## 8                North West    19-69 0.64274078
## 9  North East and Yorkshire    19-69 0.64437753
## 10                 Midlands    19-69 0.63876675
## 11          East of England    19-69 0.63034229
## 12                   London    19-69 0.67820084
## 13               South East    19-69 0.63267336
## 14               South West    19-69 0.63176131
## 15               North West   70-120 0.13187323
## 16 North East and Yorkshire   70-120 0.13685797
## 17                 Midlands   70-120 0.13558669
## 18          East of England   70-120 0.14154988
## 19                   London   70-120 0.08415135
## 20               South East   70-120 0.14273853
## 21               South West   70-120 0.16024072

Finally, we import publically available deaths per NHS region:


dth <- import_deaths() %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

#truncation to account for reporting delay
delay_max <- 21

dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
##     date_report               nhs_region deaths
## 1    2020-03-01          East of England      0
## 2    2020-03-02          East of England      1
## 3    2020-03-03          East of England      0
## 4    2020-03-04          East of England      0
## 5    2020-03-05          East of England      0
## 6    2020-03-06          East of England      1
## 7    2020-03-07          East of England      0
## 8    2020-03-08          East of England      0
## 9    2020-03-09          East of England      1
## 10   2020-03-10          East of England      0
## 11   2020-03-11          East of England      0
## 12   2020-03-12          East of England      0
## 13   2020-03-13          East of England      1
## 14   2020-03-14          East of England      2
## 15   2020-03-15          East of England      2
## 16   2020-03-16          East of England      1
## 17   2020-03-17          East of England      1
## 18   2020-03-18          East of England      5
## 19   2020-03-19          East of England      4
## 20   2020-03-20          East of England      2
## 21   2020-03-21          East of England     11
## 22   2020-03-22          East of England     11
## 23   2020-03-23          East of England     11
## 24   2020-03-24          East of England     19
## 25   2020-03-25          East of England     26
## 26   2020-03-26          East of England     36
## 27   2020-03-27          East of England     38
## 28   2020-03-28          East of England     28
## 29   2020-03-29          East of England     42
## 30   2020-03-30          East of England     45
## 31   2020-03-31          East of England     70
## 32   2020-04-01          East of England     61
## 33   2020-04-02          East of England     64
## 34   2020-04-03          East of England     80
## 35   2020-04-04          East of England     71
## 36   2020-04-05          East of England     76
## 37   2020-04-06          East of England     71
## 38   2020-04-07          East of England     93
## 39   2020-04-08          East of England    111
## 40   2020-04-09          East of England     87
## 41   2020-04-10          East of England     74
## 42   2020-04-11          East of England     91
## 43   2020-04-12          East of England    101
## 44   2020-04-13          East of England     78
## 45   2020-04-14          East of England     61
## 46   2020-04-15          East of England     82
## 47   2020-04-16          East of England     74
## 48   2020-04-17          East of England     86
## 49   2020-04-18          East of England     63
## 50   2020-04-19          East of England     67
## 51   2020-04-20          East of England     67
## 52   2020-04-21          East of England     74
## 53   2020-04-22          East of England     67
## 54   2020-04-23          East of England     49
## 55   2020-04-24          East of England     66
## 56   2020-04-25          East of England     54
## 57   2020-04-26          East of England     48
## 58   2020-04-27          East of England     46
## 59   2020-04-28          East of England     58
## 60   2020-04-29          East of England     32
## 61   2020-04-30          East of England     44
## 62   2020-05-01          East of England     49
## 63   2020-05-02          East of England     29
## 64   2020-05-03          East of England     41
## 65   2020-05-04          East of England     19
## 66   2020-05-05          East of England     35
## 67   2020-05-06          East of England     28
## 68   2020-05-07          East of England     33
## 69   2020-05-08          East of England     32
## 70   2020-05-09          East of England     28
## 71   2020-05-10          East of England     22
## 72   2020-05-11          East of England     18
## 73   2020-05-12          East of England     21
## 74   2020-05-13          East of England     27
## 75   2020-05-14          East of England     25
## 76   2020-05-15          East of England     19
## 77   2020-05-16          East of England     26
## 78   2020-05-17          East of England     17
## 79   2020-05-18          East of England     23
## 80   2020-05-19          East of England     15
## 81   2020-05-20          East of England     26
## 82   2020-05-21          East of England     21
## 83   2020-05-22          East of England     13
## 84   2020-05-23          East of England     12
## 85   2020-05-24          East of England     16
## 86   2020-05-25          East of England     25
## 87   2020-05-26          East of England     13
## 88   2020-05-27          East of England     12
## 89   2020-05-28          East of England     13
## 90   2020-05-29          East of England      8
## 91   2020-05-30          East of England      2
## 92   2020-03-01                   London      0
## 93   2020-03-02                   London      0
## 94   2020-03-03                   London      0
## 95   2020-03-04                   London      0
## 96   2020-03-05                   London      0
## 97   2020-03-06                   London      1
## 98   2020-03-07                   London      1
## 99   2020-03-08                   London      0
## 100  2020-03-09                   London      1
## 101  2020-03-10                   London      0
## 102  2020-03-11                   London      7
## 103  2020-03-12                   London      6
## 104  2020-03-13                   London     10
## 105  2020-03-14                   London     14
## 106  2020-03-15                   London     10
## 107  2020-03-16                   London     17
## 108  2020-03-17                   London     25
## 109  2020-03-18                   London     31
## 110  2020-03-19                   London     25
## 111  2020-03-20                   London     45
## 112  2020-03-21                   London     50
## 113  2020-03-22                   London     54
## 114  2020-03-23                   London     63
## 115  2020-03-24                   London     86
## 116  2020-03-25                   London    112
## 117  2020-03-26                   London    130
## 118  2020-03-27                   London    130
## 119  2020-03-28                   London    122
## 120  2020-03-29                   London    147
## 121  2020-03-30                   London    149
## 122  2020-03-31                   London    180
## 123  2020-04-01                   London    201
## 124  2020-04-02                   London    189
## 125  2020-04-03                   London    196
## 126  2020-04-04                   London    229
## 127  2020-04-05                   London    194
## 128  2020-04-06                   London    198
## 129  2020-04-07                   London    219
## 130  2020-04-08                   London    236
## 131  2020-04-09                   London    202
## 132  2020-04-10                   London    168
## 133  2020-04-11                   London    175
## 134  2020-04-12                   London    156
## 135  2020-04-13                   London    165
## 136  2020-04-14                   London    142
## 137  2020-04-15                   London    142
## 138  2020-04-16                   London    138
## 139  2020-04-17                   London     99
## 140  2020-04-18                   London    101
## 141  2020-04-19                   London    102
## 142  2020-04-20                   London     94
## 143  2020-04-21                   London     93
## 144  2020-04-22                   London    108
## 145  2020-04-23                   London     77
## 146  2020-04-24                   London     71
## 147  2020-04-25                   London     57
## 148  2020-04-26                   London     53
## 149  2020-04-27                   London     51
## 150  2020-04-28                   London     43
## 151  2020-04-29                   London     44
## 152  2020-04-30                   London     39
## 153  2020-05-01                   London     41
## 154  2020-05-02                   London     40
## 155  2020-05-03                   London     36
## 156  2020-05-04                   London     29
## 157  2020-05-05                   London     25
## 158  2020-05-06                   London     35
## 159  2020-05-07                   London     35
## 160  2020-05-08                   London     29
## 161  2020-05-09                   London     22
## 162  2020-05-10                   London     25
## 163  2020-05-11                   London     17
## 164  2020-05-12                   London     18
## 165  2020-05-13                   London     16
## 166  2020-05-14                   London     20
## 167  2020-05-15                   London     18
## 168  2020-05-16                   London     14
## 169  2020-05-17                   London     15
## 170  2020-05-18                   London      9
## 171  2020-05-19                   London     13
## 172  2020-05-20                   London     19
## 173  2020-05-21                   London     12
## 174  2020-05-22                   London     10
## 175  2020-05-23                   London      5
## 176  2020-05-24                   London      7
## 177  2020-05-25                   London      8
## 178  2020-05-26                   London     12
## 179  2020-05-27                   London      7
## 180  2020-05-28                   London      5
## 181  2020-05-29                   London      5
## 182  2020-05-30                   London      2
## 183  2020-03-01                 Midlands      0
## 184  2020-03-02                 Midlands      0
## 185  2020-03-03                 Midlands      1
## 186  2020-03-04                 Midlands      0
## 187  2020-03-05                 Midlands      0
## 188  2020-03-06                 Midlands      0
## 189  2020-03-07                 Midlands      0
## 190  2020-03-08                 Midlands      3
## 191  2020-03-09                 Midlands      1
## 192  2020-03-10                 Midlands      0
## 193  2020-03-11                 Midlands      2
## 194  2020-03-12                 Midlands      6
## 195  2020-03-13                 Midlands      5
## 196  2020-03-14                 Midlands      4
## 197  2020-03-15                 Midlands      5
## 198  2020-03-16                 Midlands     11
## 199  2020-03-17                 Midlands      8
## 200  2020-03-18                 Midlands     13
## 201  2020-03-19                 Midlands      8
## 202  2020-03-20                 Midlands     28
## 203  2020-03-21                 Midlands     13
## 204  2020-03-22                 Midlands     31
## 205  2020-03-23                 Midlands     33
## 206  2020-03-24                 Midlands     41
## 207  2020-03-25                 Midlands     48
## 208  2020-03-26                 Midlands     64
## 209  2020-03-27                 Midlands     72
## 210  2020-03-28                 Midlands     89
## 211  2020-03-29                 Midlands     92
## 212  2020-03-30                 Midlands     90
## 213  2020-03-31                 Midlands    123
## 214  2020-04-01                 Midlands    140
## 215  2020-04-02                 Midlands    142
## 216  2020-04-03                 Midlands    124
## 217  2020-04-04                 Midlands    150
## 218  2020-04-05                 Midlands    164
## 219  2020-04-06                 Midlands    140
## 220  2020-04-07                 Midlands    123
## 221  2020-04-08                 Midlands    185
## 222  2020-04-09                 Midlands    138
## 223  2020-04-10                 Midlands    127
## 224  2020-04-11                 Midlands    142
## 225  2020-04-12                 Midlands    138
## 226  2020-04-13                 Midlands    120
## 227  2020-04-14                 Midlands    116
## 228  2020-04-15                 Midlands    147
## 229  2020-04-16                 Midlands    101
## 230  2020-04-17                 Midlands    118
## 231  2020-04-18                 Midlands    115
## 232  2020-04-19                 Midlands     91
## 233  2020-04-20                 Midlands    107
## 234  2020-04-21                 Midlands     86
## 235  2020-04-22                 Midlands     77
## 236  2020-04-23                 Midlands    102
## 237  2020-04-24                 Midlands     79
## 238  2020-04-25                 Midlands     72
## 239  2020-04-26                 Midlands     81
## 240  2020-04-27                 Midlands     74
## 241  2020-04-28                 Midlands     68
## 242  2020-04-29                 Midlands     53
## 243  2020-04-30                 Midlands     55
## 244  2020-05-01                 Midlands     64
## 245  2020-05-02                 Midlands     51
## 246  2020-05-03                 Midlands     52
## 247  2020-05-04                 Midlands     61
## 248  2020-05-05                 Midlands     58
## 249  2020-05-06                 Midlands     57
## 250  2020-05-07                 Midlands     48
## 251  2020-05-08                 Midlands     34
## 252  2020-05-09                 Midlands     37
## 253  2020-05-10                 Midlands     41
## 254  2020-05-11                 Midlands     32
## 255  2020-05-12                 Midlands     45
## 256  2020-05-13                 Midlands     38
## 257  2020-05-14                 Midlands     33
## 258  2020-05-15                 Midlands     39
## 259  2020-05-16                 Midlands     34
## 260  2020-05-17                 Midlands     30
## 261  2020-05-18                 Midlands     33
## 262  2020-05-19                 Midlands     32
## 263  2020-05-20                 Midlands     36
## 264  2020-05-21                 Midlands     32
## 265  2020-05-22                 Midlands     26
## 266  2020-05-23                 Midlands     29
## 267  2020-05-24                 Midlands     18
## 268  2020-05-25                 Midlands     24
## 269  2020-05-26                 Midlands     29
## 270  2020-05-27                 Midlands     27
## 271  2020-05-28                 Midlands     22
## 272  2020-05-29                 Midlands      9
## 273  2020-05-30                 Midlands      1
## 274  2020-03-01 North East and Yorkshire      0
## 275  2020-03-02 North East and Yorkshire      0
## 276  2020-03-03 North East and Yorkshire      0
## 277  2020-03-04 North East and Yorkshire      0
## 278  2020-03-05 North East and Yorkshire      0
## 279  2020-03-06 North East and Yorkshire      0
## 280  2020-03-07 North East and Yorkshire      0
## 281  2020-03-08 North East and Yorkshire      0
## 282  2020-03-09 North East and Yorkshire      0
## 283  2020-03-10 North East and Yorkshire      0
## 284  2020-03-11 North East and Yorkshire      0
## 285  2020-03-12 North East and Yorkshire      0
## 286  2020-03-13 North East and Yorkshire      0
## 287  2020-03-14 North East and Yorkshire      0
## 288  2020-03-15 North East and Yorkshire      2
## 289  2020-03-16 North East and Yorkshire      3
## 290  2020-03-17 North East and Yorkshire      1
## 291  2020-03-18 North East and Yorkshire      2
## 292  2020-03-19 North East and Yorkshire      6
## 293  2020-03-20 North East and Yorkshire      5
## 294  2020-03-21 North East and Yorkshire      6
## 295  2020-03-22 North East and Yorkshire      7
## 296  2020-03-23 North East and Yorkshire      9
## 297  2020-03-24 North East and Yorkshire      7
## 298  2020-03-25 North East and Yorkshire     18
## 299  2020-03-26 North East and Yorkshire     21
## 300  2020-03-27 North East and Yorkshire     28
## 301  2020-03-28 North East and Yorkshire     35
## 302  2020-03-29 North East and Yorkshire     38
## 303  2020-03-30 North East and Yorkshire     64
## 304  2020-03-31 North East and Yorkshire     60
## 305  2020-04-01 North East and Yorkshire     67
## 306  2020-04-02 North East and Yorkshire     74
## 307  2020-04-03 North East and Yorkshire    100
## 308  2020-04-04 North East and Yorkshire    105
## 309  2020-04-05 North East and Yorkshire     92
## 310  2020-04-06 North East and Yorkshire     96
## 311  2020-04-07 North East and Yorkshire    102
## 312  2020-04-08 North East and Yorkshire    107
## 313  2020-04-09 North East and Yorkshire    111
## 314  2020-04-10 North East and Yorkshire    117
## 315  2020-04-11 North East and Yorkshire     98
## 316  2020-04-12 North East and Yorkshire     84
## 317  2020-04-13 North East and Yorkshire     94
## 318  2020-04-14 North East and Yorkshire    107
## 319  2020-04-15 North East and Yorkshire     96
## 320  2020-04-16 North East and Yorkshire    103
## 321  2020-04-17 North East and Yorkshire     87
## 322  2020-04-18 North East and Yorkshire     95
## 323  2020-04-19 North East and Yorkshire     88
## 324  2020-04-20 North East and Yorkshire    100
## 325  2020-04-21 North East and Yorkshire     76
## 326  2020-04-22 North East and Yorkshire     84
## 327  2020-04-23 North East and Yorkshire     62
## 328  2020-04-24 North East and Yorkshire     72
## 329  2020-04-25 North East and Yorkshire     69
## 330  2020-04-26 North East and Yorkshire     63
## 331  2020-04-27 North East and Yorkshire     65
## 332  2020-04-28 North East and Yorkshire     57
## 333  2020-04-29 North East and Yorkshire     69
## 334  2020-04-30 North East and Yorkshire     57
## 335  2020-05-01 North East and Yorkshire     64
## 336  2020-05-02 North East and Yorkshire     48
## 337  2020-05-03 North East and Yorkshire     39
## 338  2020-05-04 North East and Yorkshire     49
## 339  2020-05-05 North East and Yorkshire     40
## 340  2020-05-06 North East and Yorkshire     50
## 341  2020-05-07 North East and Yorkshire     41
## 342  2020-05-08 North East and Yorkshire     39
## 343  2020-05-09 North East and Yorkshire     43
## 344  2020-05-10 North East and Yorkshire     39
## 345  2020-05-11 North East and Yorkshire     29
## 346  2020-05-12 North East and Yorkshire     25
## 347  2020-05-13 North East and Yorkshire     28
## 348  2020-05-14 North East and Yorkshire     30
## 349  2020-05-15 North East and Yorkshire     31
## 350  2020-05-16 North East and Yorkshire     35
## 351  2020-05-17 North East and Yorkshire     26
## 352  2020-05-18 North East and Yorkshire     27
## 353  2020-05-19 North East and Yorkshire     27
## 354  2020-05-20 North East and Yorkshire     21
## 355  2020-05-21 North East and Yorkshire     30
## 356  2020-05-22 North East and Yorkshire     22
## 357  2020-05-23 North East and Yorkshire     17
## 358  2020-05-24 North East and Yorkshire     23
## 359  2020-05-25 North East and Yorkshire     20
## 360  2020-05-26 North East and Yorkshire     21
## 361  2020-05-27 North East and Yorkshire     18
## 362  2020-05-28 North East and Yorkshire     18
## 363  2020-05-29 North East and Yorkshire     18
## 364  2020-05-30 North East and Yorkshire      6
## 365  2020-03-01               North West      0
## 366  2020-03-02               North West      0
## 367  2020-03-03               North West      0
## 368  2020-03-04               North West      0
## 369  2020-03-05               North West      1
## 370  2020-03-06               North West      0
## 371  2020-03-07               North West      0
## 372  2020-03-08               North West      1
## 373  2020-03-09               North West      0
## 374  2020-03-10               North West      0
## 375  2020-03-11               North West      0
## 376  2020-03-12               North West      2
## 377  2020-03-13               North West      3
## 378  2020-03-14               North West      1
## 379  2020-03-15               North West      4
## 380  2020-03-16               North West      2
## 381  2020-03-17               North West      4
## 382  2020-03-18               North West      6
## 383  2020-03-19               North West      6
## 384  2020-03-20               North West     10
## 385  2020-03-21               North West     11
## 386  2020-03-22               North West     13
## 387  2020-03-23               North West     15
## 388  2020-03-24               North West     21
## 389  2020-03-25               North West     20
## 390  2020-03-26               North West     29
## 391  2020-03-27               North West     35
## 392  2020-03-28               North West     27
## 393  2020-03-29               North West     46
## 394  2020-03-30               North West     66
## 395  2020-03-31               North West     52
## 396  2020-04-01               North West     85
## 397  2020-04-02               North West     95
## 398  2020-04-03               North West     94
## 399  2020-04-04               North West     98
## 400  2020-04-05               North West    102
## 401  2020-04-06               North West    100
## 402  2020-04-07               North West    133
## 403  2020-04-08               North West    126
## 404  2020-04-09               North West    119
## 405  2020-04-10               North West    117
## 406  2020-04-11               North West    138
## 407  2020-04-12               North West    126
## 408  2020-04-13               North West    126
## 409  2020-04-14               North West    131
## 410  2020-04-15               North West    114
## 411  2020-04-16               North West    134
## 412  2020-04-17               North West     97
## 413  2020-04-18               North West    113
## 414  2020-04-19               North West     70
## 415  2020-04-20               North West     83
## 416  2020-04-21               North West     76
## 417  2020-04-22               North West     85
## 418  2020-04-23               North West     85
## 419  2020-04-24               North West     65
## 420  2020-04-25               North West     65
## 421  2020-04-26               North West     54
## 422  2020-04-27               North West     54
## 423  2020-04-28               North West     56
## 424  2020-04-29               North West     62
## 425  2020-04-30               North West     59
## 426  2020-05-01               North West     44
## 427  2020-05-02               North West     55
## 428  2020-05-03               North West     55
## 429  2020-05-04               North West     44
## 430  2020-05-05               North West     47
## 431  2020-05-06               North West     43
## 432  2020-05-07               North West     47
## 433  2020-05-08               North West     42
## 434  2020-05-09               North West     30
## 435  2020-05-10               North West     40
## 436  2020-05-11               North West     34
## 437  2020-05-12               North West     36
## 438  2020-05-13               North West     24
## 439  2020-05-14               North West     26
## 440  2020-05-15               North West     33
## 441  2020-05-16               North West     30
## 442  2020-05-17               North West     23
## 443  2020-05-18               North West     29
## 444  2020-05-19               North West     33
## 445  2020-05-20               North West     24
## 446  2020-05-21               North West     23
## 447  2020-05-22               North West     25
## 448  2020-05-23               North West     29
## 449  2020-05-24               North West     26
## 450  2020-05-25               North West     30
## 451  2020-05-26               North West     25
## 452  2020-05-27               North West     25
## 453  2020-05-28               North West     20
## 454  2020-05-29               North West      9
## 455  2020-05-30               North West      2
## 456  2020-03-01               South East      0
## 457  2020-03-02               South East      0
## 458  2020-03-03               South East      1
## 459  2020-03-04               South East      0
## 460  2020-03-05               South East      1
## 461  2020-03-06               South East      0
## 462  2020-03-07               South East      0
## 463  2020-03-08               South East      1
## 464  2020-03-09               South East      1
## 465  2020-03-10               South East      1
## 466  2020-03-11               South East      1
## 467  2020-03-12               South East      0
## 468  2020-03-13               South East      1
## 469  2020-03-14               South East      1
## 470  2020-03-15               South East      5
## 471  2020-03-16               South East      8
## 472  2020-03-17               South East      7
## 473  2020-03-18               South East     10
## 474  2020-03-19               South East      9
## 475  2020-03-20               South East     13
## 476  2020-03-21               South East      7
## 477  2020-03-22               South East     25
## 478  2020-03-23               South East     20
## 479  2020-03-24               South East     22
## 480  2020-03-25               South East     29
## 481  2020-03-26               South East     34
## 482  2020-03-27               South East     34
## 483  2020-03-28               South East     36
## 484  2020-03-29               South East     54
## 485  2020-03-30               South East     58
## 486  2020-03-31               South East     65
## 487  2020-04-01               South East     65
## 488  2020-04-02               South East     55
## 489  2020-04-03               South East     72
## 490  2020-04-04               South East     80
## 491  2020-04-05               South East     82
## 492  2020-04-06               South East     88
## 493  2020-04-07               South East    100
## 494  2020-04-08               South East     82
## 495  2020-04-09               South East    104
## 496  2020-04-10               South East     88
## 497  2020-04-11               South East     87
## 498  2020-04-12               South East     88
## 499  2020-04-13               South East     83
## 500  2020-04-14               South East     65
## 501  2020-04-15               South East     72
## 502  2020-04-16               South East     56
## 503  2020-04-17               South East     86
## 504  2020-04-18               South East     57
## 505  2020-04-19               South East     69
## 506  2020-04-20               South East     85
## 507  2020-04-21               South East     49
## 508  2020-04-22               South East     54
## 509  2020-04-23               South East     57
## 510  2020-04-24               South East     64
## 511  2020-04-25               South East     50
## 512  2020-04-26               South East     51
## 513  2020-04-27               South East     40
## 514  2020-04-28               South East     40
## 515  2020-04-29               South East     46
## 516  2020-04-30               South East     29
## 517  2020-05-01               South East     37
## 518  2020-05-02               South East     35
## 519  2020-05-03               South East     17
## 520  2020-05-04               South East     35
## 521  2020-05-05               South East     29
## 522  2020-05-06               South East     25
## 523  2020-05-07               South East     25
## 524  2020-05-08               South East     25
## 525  2020-05-09               South East     28
## 526  2020-05-10               South East     19
## 527  2020-05-11               South East     23
## 528  2020-05-12               South East     26
## 529  2020-05-13               South East     18
## 530  2020-05-14               South East     31
## 531  2020-05-15               South East     23
## 532  2020-05-16               South East     20
## 533  2020-05-17               South East     16
## 534  2020-05-18               South East     18
## 535  2020-05-19               South East     12
## 536  2020-05-20               South East     22
## 537  2020-05-21               South East     13
## 538  2020-05-22               South East     16
## 539  2020-05-23               South East     17
## 540  2020-05-24               South East     15
## 541  2020-05-25               South East     12
## 542  2020-05-26               South East     15
## 543  2020-05-27               South East     12
## 544  2020-05-28               South East      8
## 545  2020-05-29               South East      0
## 546  2020-05-30               South East      0
## 547  2020-03-01               South West      0
## 548  2020-03-02               South West      0
## 549  2020-03-03               South West      0
## 550  2020-03-04               South West      0
## 551  2020-03-05               South West      0
## 552  2020-03-06               South West      0
## 553  2020-03-07               South West      0
## 554  2020-03-08               South West      0
## 555  2020-03-09               South West      0
## 556  2020-03-10               South West      0
## 557  2020-03-11               South West      1
## 558  2020-03-12               South West      0
## 559  2020-03-13               South West      0
## 560  2020-03-14               South West      1
## 561  2020-03-15               South West      0
## 562  2020-03-16               South West      0
## 563  2020-03-17               South West      2
## 564  2020-03-18               South West      2
## 565  2020-03-19               South West      5
## 566  2020-03-20               South West      3
## 567  2020-03-21               South West      6
## 568  2020-03-22               South West      9
## 569  2020-03-23               South West      9
## 570  2020-03-24               South West      7
## 571  2020-03-25               South West      9
## 572  2020-03-26               South West     11
## 573  2020-03-27               South West     13
## 574  2020-03-28               South West     21
## 575  2020-03-29               South West     18
## 576  2020-03-30               South West     23
## 577  2020-03-31               South West     23
## 578  2020-04-01               South West     22
## 579  2020-04-02               South West     23
## 580  2020-04-03               South West     30
## 581  2020-04-04               South West     42
## 582  2020-04-05               South West     32
## 583  2020-04-06               South West     34
## 584  2020-04-07               South West     39
## 585  2020-04-08               South West     47
## 586  2020-04-09               South West     24
## 587  2020-04-10               South West     46
## 588  2020-04-11               South West     43
## 589  2020-04-12               South West     23
## 590  2020-04-13               South West     26
## 591  2020-04-14               South West     24
## 592  2020-04-15               South West     31
## 593  2020-04-16               South West     29
## 594  2020-04-17               South West     33
## 595  2020-04-18               South West     25
## 596  2020-04-19               South West     31
## 597  2020-04-20               South West     26
## 598  2020-04-21               South West     26
## 599  2020-04-22               South West     22
## 600  2020-04-23               South West     17
## 601  2020-04-24               South West     19
## 602  2020-04-25               South West     15
## 603  2020-04-26               South West     27
## 604  2020-04-27               South West     13
## 605  2020-04-28               South West     17
## 606  2020-04-29               South West     14
## 607  2020-04-30               South West     26
## 608  2020-05-01               South West      6
## 609  2020-05-02               South West      7
## 610  2020-05-03               South West     10
## 611  2020-05-04               South West     16
## 612  2020-05-05               South West     14
## 613  2020-05-06               South West     18
## 614  2020-05-07               South West     16
## 615  2020-05-08               South West      5
## 616  2020-05-09               South West     10
## 617  2020-05-10               South West      5
## 618  2020-05-11               South West      7
## 619  2020-05-12               South West      7
## 620  2020-05-13               South West      7
## 621  2020-05-14               South West      6
## 622  2020-05-15               South West      3
## 623  2020-05-16               South West      4
## 624  2020-05-17               South West      6
## 625  2020-05-18               South West      4
## 626  2020-05-19               South West      6
## 627  2020-05-20               South West      1
## 628  2020-05-21               South West      9
## 629  2020-05-22               South West      6
## 630  2020-05-23               South West      6
## 631  2020-05-24               South West      3
## 632  2020-05-25               South West      7
## 633  2020-05-26               South West     10
## 634  2020-05-27               South West      5
## 635  2020-05-28               South West      8
## 636  2020-05-29               South West      2
## 637  2020-05-30               South West      2

1.5 Completion date

We extract the completion date from the NHS Pathways file timestamp:


database_date <- attr(x, "timestamp")
database_date
## [1] "2020-05-31"

The completion date of the NHS Pathways data is Sunday 31 May 2020.

1.6 Add variables

We add the following variable:

  • day: an integer representing the number of days from the earliest data reported, used for modelling purposes; the first day is 0

x <- x %>% 
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)),
         nhs_region = gsub(" Of ", " of ", nhs_region),
         nhs_region = gsub(" And ", " and ", nhs_region),
         day = as.integer(date - min(date, na.rm = TRUE)))

1.7 Auxiliary functions

These are functions which will be used further in the analyses.

Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:


## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here

Rsq <- function(x) {
  1 - (x$deviance / x$null.deviance)
}

Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:


## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals

get_r <- function(model) {
  ##  extract coefficients and conf int
  out <- data.frame(r = coef(model))  %>%
    rownames_to_column("var") %>% 
    cbind(confint(model)) %>%
    filter(!grepl("day_of_week", var)) %>% 
    filter(grepl("day", var)) %>%
    rename(lower_95 = "2.5 %",
           upper_95 = "97.5 %") %>%
    mutate(var = sub("day:", "", var))
  
  ## reconstruct values: intercept + region-coefficient
  for (i in 2:nrow(out)) {
    out[i, -1] <- out[1, -1] + out[i, -1]
  }
  
  ## find the name of the intercept, restore regions names
  out <- out %>%
    mutate(nhs_region = model$xlevels$nhs_region) %>%
    select(nhs_region, everything(), -var)
  
  ## find halving times
  halving <- log(0.5) / out[,-1] %>%
    rename(halving_t = r,
           halving_t_lower_95 = lower_95,
           halving_t_upper_95 = upper_95)
  
  ## set halving times with exclusion intervals to NA
  no_halving <- out$lower_95 < 0 & out$upper_95 > 0
  halving[no_halving, ] <- NA_real_
  
  ## return all data
  cbind(out, halving)
  
}

Functions used in the correlation analysis between NHS Pathways reports and deaths:

## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.

getcor <- function(x, ndx) {
  return(cor(x$deaths[ndx],
             x$note_lag[ndx],
             use = "complete.obs",
             method = "pearson"))
}

## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)

getboot <- function(x) {
  result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000), 
                           type = "bca")
  return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
                    r = result$t0,
                    r_low = result$bca[4],
                    r_hi = result$bca[5]))
}

Function to classify the day of the week into weekend, Monday, and the rest:


## Fn to add day of week
day_of_week <- function(df) {
  df %>% 
    dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>% 
    dplyr::mutate(day_of_week = dplyr::case_when(
      day_of_week %in% c("Sat", "Sun") ~ "weekend",
      day_of_week %in% c("Mon") ~ "monday",
      !(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
    ) %>% 
      factor(levels = c("rest_of_week", "monday", "weekend")))
}

Custom color palettes, color scales, and vectors of colors:


pal <- c("#006212",
         "#ae3cab",
         "#00db90",
         "#960c00",
         "#55aaff",
         "#ff7e78",
         "#00388d")

age.pal <- viridis::viridis(3,begin = 0.1, end = 0.7)

3 Comparison with deaths time series

3.1 Outline

We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.

Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.

3.2 Lagged correlation

We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.

First we join the NHS Pathways and death data, and aggregate over all England:

## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max

dth_trunc <- dth %>%
  rename(date = date_report) %>%
  filter(date <= trunc_date) 

## join with notification data
all_data <- x %>% 
  filter(!is.na(nhs_region)) %>%
  group_by(date, nhs_region) %>%
  summarise(count = sum(count, na.rm = T)) %>%
  ungroup %>%
  inner_join(dth_trunc,
             by = c("date","nhs_region"))

all_tot <- all_data %>%
  group_by(date) %>%
  summarise(count = sum(count, na.rm = TRUE),
            deaths = sum(deaths, na.rm = TRUE)) 

We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:


## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
  
  ## lag reports
  summary <- all_tot %>% 
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI
    getboot(.) %>%
    mutate(lag = i)

  lag_cor <- bind_rows(lag_cor, summary)
}

cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
  theme_bw() +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_point() +
  geom_line() +
  labs(x = "Lag between NHS pathways and death data (days)",
       y = "Pearson's correlation") +
  large_txt
cor_vs_lag


l_opt <- which.max(lag_cor$r)

This analysis suggests that the best lag is 16 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 16 days.


all_tot <- all_tot %>%
  rename(date_death = date) %>%
  mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
         note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
         date_note = lag(date_death,16))

lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")

summary(lag_mod)
## 
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -6.5247  -3.3738   0.1468   2.2236   7.9704  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.529e+00  5.667e-02   97.55   <2e-16 ***
## note_lag    8.525e-06  5.571e-07   15.30   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 12.23152)
## 
##     Null deviance: 3280.1  on 36  degrees of freedom
## Residual deviance:  429.0  on 35  degrees of freedom
##   (16 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 4

exp(coefficients(lag_mod))
## (Intercept)    note_lag 
##  251.823837    1.000009
exp(confint(lag_mod))
##                  2.5 %    97.5 %
## (Intercept) 225.086718 281.08822
## note_lag      1.000007   1.00001

Rsq(lag_mod)
## [1] 0.8692093

mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])

all_tot_pred <- 
  all_tot %>%
  filter(!is.na(note_lag)) %>%
  mutate(pred = mod_fit$fit,
         pred.se = mod_fit$se.fit,
         low = exp(pred - 1.96*pred.se),
         hi = exp(pred + 1.96*pred.se))


glm_fit <- all_tot_pred %>% 
    filter(!is.na(note_lag)) %>%
  ggplot(aes(x = note_lag, y = deaths)) +
  geom_point() + 
  geom_line(aes(y = exp(pred))) + 
  geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
  theme_bw() +
  labs(y = "Daily number of\ndeaths reported",
       x = "Daily number of NHS Pathways reports") +
  large_txt

glm_fit

4 Supplementary figures

4.1 Serial interval distribution

This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.

SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale, w = 0.5)

SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
                                        meanlog = log(4.7),
                                        sdlog = log(2.9), w = 0.5)

SI_dist1 <- data.frame(x = SI_distribution$r(1e5)) 
SI_dist1 <- count(SI_dist1, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 30, 5)) +
    theme_bw()

SI_dist2 <- data.frame(x = SI_distribution2$r(1e5)) 
SI_dist2 <- count(SI_dist2, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
    theme_bw()


ggpubr::ggarrange(SI_dist1,
                  SI_dist2,
                  nrow = 1,
                  labels = "AUTO") 

4.2 Sensitivity analysis - 7 or 21 days moving window

We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.

First with the 7 days window:

## set moving time window (1/2/3 weeks)
w <- 7

# create empty df
r_all_sliding_7days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
plot_R <- r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_7days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_7days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_7 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

Then with the 21 days window:

## set moving time window (1/2/3 weeks)
w <- 21

# create empty df
r_all_sliding_21days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
# plot
plot_R <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_21days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_21days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_21 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

And we combine both outputs into a single plot:


ggpubr::ggarrange(r_R_7,
                  r_R_21,
                  nrow = 2,
                  labels = "AUTO",
                  common.legend = TRUE,
                  legend = "bottom") 

4.3 Correlation between NHS Pathways reports and deaths by NHS region


lag_cor_reg <- data.frame()

for (i in 0:30) {

  summary <-
    all_data %>%
    group_by(nhs_region) %>%
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI for each region
    group_modify(~getboot(.x)) %>%
    mutate(lag = i)
  
  lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}

cor_vs_lag_reg <- 
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
  geom_point() +
  geom_line() +
  facet_wrap(~nhs_region) +
  scale_color_manual(values = pal) +
  scale_fill_manual(values = pal, guide = F) +  
  theme_bw() +
  labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(override.aes = list(fill = NA)))

cor_vs_lag_reg

5 Export data

We save the tables created during our analysis:


if (!dir.exists("excel_tables")) {
  dir.create("excel_tables")
}


## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")

for (e in tables_to_export) {
  rio::export(get(e),
              file.path("excel_tables",
                        paste0(e, ".xlsx")))
}

## also export result from regression on lagged data 
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))

6 System information

6.1 Outline

The following information documents the system on which the document was compiled.

6.2 System

This provides information on the operating system.

Sys.info()
##                                                                                             sysname 
##                                                                                            "Darwin" 
##                                                                                             release 
##                                                                                            "19.4.0" 
##                                                                                             version 
## "Darwin Kernel Version 19.4.0: Wed Mar  4 22:28:40 PST 2020; root:xnu-6153.101.6~15/RELEASE_X86_64" 
##                                                                                            nodename 
##                                                                                    "Mac-1589.local" 
##                                                                                             machine 
##                                                                                            "x86_64" 
##                                                                                               login 
##                                                                                              "root" 
##                                                                                                user 
##                                                                                            "runner" 
##                                                                                      effective_user 
##                                                                                            "runner"

6.3 R environment

This provides information on the version of R used:

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.3                         
## year           2020                        
## month          02                          
## day            29                          
## svn rev        77875                       
## language       R                           
## version.string R version 3.6.3 (2020-02-29)
## nickname       Holding the Windsock

6.4 R packages

This provides information on the packages used:

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.1     ggpubr_0.3.0         lubridate_1.7.8     
##  [4] chngpt_2020.5-21     cyphr_1.1.0          DT_0.13             
##  [7] kableExtra_1.1.0     janitor_2.0.1        remotes_2.1.1       
## [10] projections_0.4.1    earlyR_0.0.1         epitrix_0.2.2       
## [13] distcrete_1.0.3      incidence_1.7.1      rio_0.5.16          
## [16] reshape2_1.4.4       rvest_0.3.5          xml2_1.3.2          
## [19] linelist_0.0.40.9000 forcats_0.5.0        stringr_1.4.0       
## [22] dplyr_1.0.0          purrr_0.3.4          readr_1.3.1         
## [25] tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.1       
## [28] tidyverse_1.3.0      here_0.1             reportfactory_0.0.5 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1  selectr_0.4-2     ggsignif_0.6.0    ellipsis_0.3.1   
##  [5] rprojroot_1.3-2   snakecase_0.11.0  fs_1.4.1          rstudioapi_0.11  
##  [9] farver_2.0.3      fansi_0.4.1       splines_3.6.3     knitr_1.28       
## [13] jsonlite_1.6.1    broom_0.5.6       dbplyr_1.4.4      compiler_3.6.3   
## [17] httr_1.4.1        backports_1.1.7   assertthat_0.2.1  Matrix_1.2-18    
## [21] cli_2.0.2         htmltools_0.4.0   prettyunits_1.1.1 tools_3.6.3      
## [25] gtable_0.3.0      glue_1.4.1        Rcpp_1.0.4.6      carData_3.0-4    
## [29] cellranger_1.1.0  vctrs_0.3.0       nlme_3.1-144      matchmaker_0.1.1 
## [33] crosstalk_1.1.0.1 xfun_0.14         ps_1.3.3          openxlsx_4.1.5   
## [37] lifecycle_0.2.0   rstatix_0.5.0     MASS_7.3-51.5     scales_1.1.1     
## [41] hms_0.5.3         sodium_1.1        yaml_2.2.1        curl_4.3         
## [45] gridExtra_2.3     stringi_1.4.6     kyotil_2019.11-22 boot_1.3-24      
## [49] pkgbuild_1.0.8    zip_2.0.4         rlang_0.4.6       pkgconfig_2.0.3  
## [53] evaluate_0.14     lattice_0.20-38   labeling_0.3      htmlwidgets_1.5.1
## [57] cowplot_1.0.0     processx_3.4.2    tidyselect_1.1.0  plyr_1.8.6       
## [61] magrittr_1.5      R6_2.4.1          generics_0.0.2    DBI_1.1.0        
## [65] pillar_1.4.4      haven_2.3.0       foreign_0.8-75    withr_2.2.0      
## [69] mgcv_1.8-31       survival_3.1-8    abind_1.4-5       modelr_0.1.8     
## [73] crayon_1.3.4      car_3.0-8         utf8_1.1.4        rmarkdown_2.2    
## [77] viridis_0.5.1     grid_3.6.3        readxl_1.3.1      data.table_1.12.8
## [81] blob_1.2.1        callr_3.4.3       reprex_0.3.0      digest_0.6.25    
## [85] webshot_0.5.2     munsell_0.5.0     viridisLite_0.3.0